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Scikit Digital Health (SKDH) is a Python package with methods for ingesting and analyzing wearable inertial sensor data.

• Documentation:https://scikit-digital-health.readthedocs.io/en/latest/
• Bug reports:https://github.com/PfizerRD/scikit-digital-health/issues
• Contributing:https://scikit-digital-health.readthedocs.io/en/latest/src/dev/contributing.html

SKDH provides the following:

• Methods for ingesting data from binary file formats (ie Axivity, GeneActiv)
• Preprocessing of accelerometer data
• Common time-series signal features
• Common time-series/inertial data analysis functions
• Inertial data analysis algorithms (ie gait, sit-to-stand, sleep, activity)

SKDH is available on bothconda-forge andPyPI.

conda install scikit-digital-health -c conda-forge

or

pip install scikit-digital-health

As of 0.9.15, Scikit Digital Health is built using Meson.

An example notebook can be found in the examples folder:SKDH tutorialalong with some sample data. The tutorial walks through running individual modules in SKDH,then building up a pipeline, and finally creating a new module.

If you use SKDH in your research, please include the following citation:

[1]L. Adamowicz, Y. Christakis, M. D. Czech, and T. Adamusiak, “SciKit Digital Health: Python Package for Streamlined Wearable Inertial Sensor Data Processing,” JMIR mHealth and uHealth, vol. 10, no. 4, p. e36762, Apr. 2022, doi: 10.2196/36762.